基于测量数据的自适应探测系统技术方法研究
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摘要
针对传统的光电探测系统大多只根据单一的专项任务进行设计,无法兼顾不同任务探测识别的需求,提出了一种基于测量数据的自适应智能光电探测系统的概念。阐述了针对不同任务需求,在轨实现测量数据的处理分析,自适应修正系统参数的方法。重点介绍了系统参数案例库的建立,利用傅里叶分光技术判断场景目标,自适应选择工作波段的方法。针对面目标和点目标探测,分别利用清晰度评价函数和综合信噪比作为调参依据,形成调参反馈,自适应调整光学系统口径、增益、焦距和积分时间等参数,达到最佳成像模式,实现多种类不同目标探测和识别的需求。
Traditional photoelectric detection systems are designed for a single special task and cannot meet the needs of detection and identification for different tasks. Therefore, a concept of adaptive intelligent photoelectric detection system based on measurement data is proposed. The method of self-adaptively correcting the system parameters by onboard autonomous data processing and analysis of different task requirements is described. This paper introduces the establishment of the system parameter case library and the method of determining the scenes and targets and adaptively selecting the operating wavelength band using Fourier spectroscopy. For surface targets and point target detection, the sharpness evaluation function and the integrated signal-to-noise ratio are respectively used as the reference function, which provide feedback to adaptively adjust parameters such as aperture, gain, focal length and integration time, to achieve the best imaging mode and thus meet the requirements for detection and identification of multiple types of targets.
Traditional photoelectric detection systems are designed for a single special task and cannot meet the needs of detection and identification for different tasks. Therefore, a concept of adaptive intelligent photoelectric detection system based on measurement data is proposed. The method of self-adaptively correcting the system parameters by onboard autonomous data processing and analysis of different task requirements is described. This paper introduces the establishment of the system parameter case library and the method of determining the scenes and targets and adaptively selecting the operating wavelength band using Fourier spectroscopy. For surface targets and point target detection, the sharpness evaluation function and the integrated signal-to-noise ratio are respectively used as the reference function, which provide feedback to adaptively adjust parameters such as aperture, gain, focal length and integration time, to achieve the best imaging mode and thus meet the requirements for detection and identification of multiple types of targets.
引文
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[3]Chien S,Cichy B,Jones J,et al.An autonomous earth-observing sensorweb[C]//IEEE Computer Society,2006:178-185.
[4]Chien S,Sherwood R,Tran D,et al.Onboard autonomy on the earth observing one mission[C]//AIAA 1st Intelligent Systems Technical Conference,2013:DOI:10.2514/6.2004-6515.
[5]Wilson T,Felt R.Hyperspectral remote sensing technology(HRST)program[C]//Aerospace Conference of IEEE,2013:193-200.
[6]许元男,李军伟,吴开峰,等.采用综合信噪比的中波光谱探测波段选择[J].红外与激光工程,2014,43(7):2126-2131.XU Yuannan,LI Junwei,WU Kaifeng,et al.Detection band selection for mid-wave infrared spectrum using synthetic signal-to-noise ratio[J].Infrared&Laser Engineering,2014,43(7):2126-2131.
[7]钟宇,吴晓燕,黄树彩,等.星载红外探测器信噪比模型灵敏度分析[J].红外技术,2014,36(7):582-587.ZHONG Yu,WU Xiaoyan,HUANG Shucai,et al.Sensitivity analysis of SNR model for satellite-based infrared detector[J].Infrared Technology,2014,36(7):582-587.